At Apple, we focus deeply on our customers’ experience. Apple Ads brings this same approach to advertising, helping people find exactly what they’re looking for and helping advertisers grow their businesses! Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes—from small app developers to big, global brands. Because when advertising is done right, it benefits everyone! Apple’s Ads team is seeking a highly skilled and motivated Machine Learning Engineer to join the Ads Relevance and Quality team. This team is responsible for ensuring high-quality, trustworthy ad experiences by building intelligent systems to evaluate ad relevance, detect low-quality or offensive content, and optimize user satisfaction. You’ll work at the intersection of applied ML, NLP, and content quality—designing models and systems that understand queries, flag inappropriate content, and raise the bar for ad relevance and user trust across billions of queries and impressions. You’ll play a key role in shaping the future of safe, high-quality advertising at Apple. Your work will help ensure that ads remain useful, relevant, and respectful of our users—supporting Apple’s values of privacy, trust, and transparency. You’ll collaborate with world-class engineers and researchers, apply cutting-edge ML techniques in real-world systems, and have a direct impact on the experience of millions of users every day. - Design and implement machine learning models to evaluate and improve ad relevance, trust, and quality for user queries - Build NLP and multi-modal models that detect offensive, unsafe, or policy-violating content at scale - Develop methods for semantic query understanding, ads understanding, relevance scoring, and keyword-to-ad matching - Collaborate closely with product and policy teams to translate content integrity standards into measurable ML objectives - Work with large-scale, privacy-preserving datasets to discover and operationalize new quality signals - Conduct offline/online experiments to measure impact on user trust and satisfaction across Ads - Partner cross-functionally with infrastructure, product, and moderation teams to deploy models at production scale